无需地图信息，从路口观测数据自动发现车辆行为模式。
Improving behavior profile discovery for vehicles
- 用扩展卡尔曼滤波匹配不同长度轨迹，结合EM思想聚类行为。
- 通过KL散度判断聚类是否需拆分或合并，实现动态一致性。
- 发现驾驶行为核心受主动性和交互影响，适合自动驾驶仿真研究。
已有多种方法用于在仿真中模拟真实驾驶员行为。本文提出一种新方法，仅基于对路口的无干扰观测数据，发现各类宏观动作的行为特征。利用先前工作中已识别的宏观动作，提出一种基于扩展卡尔曼滤波（EKF）的轨迹比对方法，可处理不同长度轨迹；结合受EM启发的聚类算法，确定反映实际行为的若干簇。同时引入Kullback-Leibler散度（KL）准则，判断聚类是否需要分裂或合并。最终，各宏观动作的行为模式由所发现的簇定义，不依赖环境地图信息，并与车辆运动动态保持一致。观察表明，驾驶员行为主要受主动性和与其他道路使用者交互的影响。
原文摘要 · Abstract (English)
Multiple approaches have already been proposed to mimic real driver behaviors in simulation. This article proposes a new one, based solely on the exploration of undisturbed observation of intersections. From them, the behavior profiles for each macro-maneuver will be discovered. Using the macro-maneuvers already identified in previous works, a comparison method between trajectories with different lengths using an Extended Kalman Filter (EKF) is proposed, which combined with an Expectation-Maximization (EM) inspired method, defines the different clusters that represent the behaviors observed. This is also paired with a Kullback-Liebler divergent (KL) criteria to define when the clusters need to be split or merged. Finally, the behaviors for each macro-maneuver are determined by each cluster discovered, without using any map information about the environment and being dynamically consistent with vehicle motion. By observation it becomes clear that the two main factors for driver's behavior are their assertiveness and interaction with other road users.
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